As you know, model-free learning has good asymptotic performance but poor sample complexity. On the other side, model-based learning is efficient from a data standpoint, but struggles when it comes to more complex tasks. By combining model-based and model-free approaches, it is possible to reach a smooth spot where sample complexity decreases consistently, while achieving the high performance of model-free algorithms.
There are many ways to integrate both worlds, and the algorithms that propose to do it are very different from one another. For example, when the model is given (as they are in the games of Go and Chess), search tree and value-based algorithms can help each other to produce a better action value estimate. ...
